Overview

Brought to you by YData

Dataset statistics

Number of variables37
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.5 KiB
Average record size in memory665.8 B

Variable types

Categorical5
DateTime2
Unsupported1
Numeric23
Text1
Boolean5

Alerts

season has constant value "First warm / stratified" Constant
cruise has constant value "apr_25" Constant
ammonium is highly overall correlated with phosphateHigh correlation
ammonium_imputed is highly overall correlated with nitrate_nitrite_imputed and 4 other fieldsHigh correlation
chl is highly overall correlated with depth_m and 5 other fieldsHigh correlation
chl_imputed is highly overall correlated with depth_desc and 2 other fieldsHigh correlation
co2 is highly overall correlated with co3- and 9 other fieldsHigh correlation
co3- is highly overall correlated with co2 and 8 other fieldsHigh correlation
depth_desc is highly overall correlated with chl_imputed and 4 other fieldsHigh correlation
depth_m is highly overall correlated with chl and 13 other fieldsHigh correlation
hco3- is highly overall correlated with chl and 14 other fieldsHigh correlation
lat is highly overall correlated with loc and 2 other fieldsHigh correlation
loc is highly overall correlated with lat and 3 other fieldsHigh correlation
lon is highly overall correlated with lat and 3 other fieldsHigh correlation
nitrate_nitrite is highly overall correlated with phosphateHigh correlation
nitrate_nitrite_imputed is highly overall correlated with ammonium_imputed and 4 other fieldsHigh correlation
o2 is highly overall correlated with station and 1 other fieldsHigh correlation
omega_ar is highly overall correlated with co2 and 9 other fieldsHigh correlation
omega_ca is highly overall correlated with co2 and 9 other fieldsHigh correlation
ph is highly overall correlated with co2High correlation
ph_lb is highly overall correlated with co2 and 8 other fieldsHigh correlation
phosphate is highly overall correlated with ammonium and 4 other fieldsHigh correlation
phosphate_imputed is highly overall correlated with ammonium_imputed and 4 other fieldsHigh correlation
pres is highly overall correlated with chl and 13 other fieldsHigh correlation
revelle is highly overall correlated with co2 and 11 other fieldsHigh correlation
sal_wat is highly overall correlated with chl and 8 other fieldsHigh correlation
silicate is highly overall correlated with ammonium_imputed and 3 other fieldsHigh correlation
silicate_imputed is highly overall correlated with ammonium_imputed and 4 other fieldsHigh correlation
station is highly overall correlated with lat and 4 other fieldsHigh correlation
ta is highly overall correlated with hco3- and 1 other fieldsHigh correlation
tc is highly overall correlated with chl and 7 other fieldsHigh correlation
temp_in_lb is highly overall correlated with loc and 3 other fieldsHigh correlation
temp_wat is highly overall correlated with ammonium_imputed and 15 other fieldsHigh correlation
nitrate_nitrite_imputed is highly imbalanced (64.7%) Imbalance
ammonium_imputed is highly imbalanced (64.7%) Imbalance
phosphate_imputed is highly imbalanced (64.7%) Imbalance
silicate_imputed is highly imbalanced (64.7%) Imbalance
depth_m has unique values Unique
sample_id has unique values Unique
temp_wat has unique values Unique
sal_wat has unique values Unique
pres has unique values Unique
ph_lb has unique values Unique
ta has unique values Unique
ph has unique values Unique
tc has unique values Unique
co2 has unique values Unique
hco3- has unique values Unique
co3- has unique values Unique
omega_ca has unique values Unique
omega_ar has unique values Unique
revelle has unique values Unique
datetime has unique values Unique
time is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-07-31 12:43:51.682816
Analysis finished2025-07-31 12:44:48.904025
Duration57.22 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

station
Categorical

High correlation 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
S1
S3
J1
J2
P2
Other values (7)
14 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters60
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS1
2nd rowS1
3rd rowS1
4th rowS1
5th rowS2

Common Values

ValueCountFrequency (%)
S1 4
13.3%
S3 3
10.0%
J1 3
10.0%
J2 3
10.0%
P2 3
10.0%
S2 2
6.7%
J3 2
6.7%
P1 2
6.7%
P3 2
6.7%
A1 2
6.7%
Other values (2) 4
13.3%

Length

2025-07-31T12:44:49.087758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s1 4
13.3%
s3 3
10.0%
j1 3
10.0%
j2 3
10.0%
p2 3
10.0%
s2 2
6.7%
j3 2
6.7%
p1 2
6.7%
p3 2
6.7%
a1 2
6.7%
Other values (2) 4
13.3%

Most occurring characters

ValueCountFrequency (%)
1 11
18.3%
2 10
16.7%
S 9
15.0%
3 9
15.0%
J 8
13.3%
P 7
11.7%
A 6
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 11
18.3%
2 10
16.7%
S 9
15.0%
3 9
15.0%
J 8
13.3%
P 7
11.7%
A 6
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 11
18.3%
2 10
16.7%
S 9
15.0%
3 9
15.0%
J 8
13.3%
P 7
11.7%
A 6
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 11
18.3%
2 10
16.7%
S 9
15.0%
3 9
15.0%
J 8
13.3%
P 7
11.7%
A 6
10.0%

date
Date

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2025-04-23 00:00:00
Maximum2025-04-27 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-31T12:44:49.196827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:49.337970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)

time
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size1.5 KiB

lat
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.442
Minimum5.23
Maximum5.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:49.469747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.23
5-th percentile5.266
Q15.34
median5.445
Q35.5
95-th percentile5.6285
Maximum5.66
Range0.43
Interquartile range (IQR)0.16

Descriptive statistics

Standard deviation0.11585961
Coefficient of variation (CV)0.021289895
Kurtosis-0.58302644
Mean5.442
Median Absolute Deviation (MAD)0.055
Skewness0.019481631
Sum163.26
Variance0.013423448
MonotonicityNot monotonic
2025-07-31T12:44:49.572628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5.41 5
16.7%
5.49 4
13.3%
5.31 3
10.0%
5.5 3
10.0%
5.32 3
10.0%
5.4 2
 
6.7%
5.23 2
 
6.7%
5.59 2
 
6.7%
5.66 2
 
6.7%
5.57 2
 
6.7%
ValueCountFrequency (%)
5.23 2
 
6.7%
5.31 3
10.0%
5.32 3
10.0%
5.4 2
 
6.7%
5.41 5
16.7%
5.48 2
 
6.7%
5.49 4
13.3%
5.5 3
10.0%
5.57 2
 
6.7%
5.59 2
 
6.7%
ValueCountFrequency (%)
5.66 2
 
6.7%
5.59 2
 
6.7%
5.57 2
 
6.7%
5.5 3
10.0%
5.49 4
13.3%
5.48 2
 
6.7%
5.41 5
16.7%
5.4 2
 
6.7%
5.32 3
10.0%
5.31 3
10.0%

lon
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0905
Minimum-0.18
Maximum0.42
Zeros0
Zeros (%)0.0%
Negative12
Negative (%)40.0%
Memory size372.0 B
2025-07-31T12:44:49.664006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.18
5-th percentile-0.15975
Q1-0.0725
median0.07
Q30.22875
95-th percentile0.39975
Maximum0.42
Range0.6
Interquartile range (IQR)0.30125

Descriptive statistics

Standard deviation0.19045024
Coefficient of variation (CV)2.1044225
Kurtosis-1.1303456
Mean0.0905
Median Absolute Deviation (MAD)0.16
Skewness0.28029457
Sum2.715
Variance0.036271293
MonotonicityNot monotonic
2025-07-31T12:44:49.763894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
-0.02 4
13.3%
-0.135 4
13.3%
0.195 3
10.0%
0.07 3
10.0%
-0.18 2
6.7%
0.025 2
6.7%
-0.09 2
6.7%
0.15 2
6.7%
0.24 2
6.7%
0.33 2
6.7%
Other values (2) 4
13.3%
ValueCountFrequency (%)
-0.18 2
6.7%
-0.135 4
13.3%
-0.09 2
6.7%
-0.02 4
13.3%
0.025 2
6.7%
0.07 3
10.0%
0.15 2
6.7%
0.195 3
10.0%
0.24 2
6.7%
0.33 2
6.7%
ValueCountFrequency (%)
0.42 2
6.7%
0.375 2
6.7%
0.33 2
6.7%
0.24 2
6.7%
0.195 3
10.0%
0.15 2
6.7%
0.07 3
10.0%
0.025 2
6.7%
-0.02 4
13.3%
-0.09 2
6.7%

loc
Categorical

High correlation 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Sakumono
Jamestown
Prampram
Ayitepa

Length

Max length9
Median length8
Mean length8.0666667
Min length7

Characters and Unicode

Total characters242
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSakumono
2nd rowSakumono
3rd rowSakumono
4th rowSakumono
5th rowSakumono

Common Values

ValueCountFrequency (%)
Sakumono 9
30.0%
Jamestown 8
26.7%
Prampram 7
23.3%
Ayitepa 6
20.0%

Length

2025-07-31T12:44:49.899523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-31T12:44:50.027076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sakumono 9
30.0%
jamestown 8
26.7%
prampram 7
23.3%
ayitepa 6
20.0%

Most occurring characters

ValueCountFrequency (%)
a 37
15.3%
m 31
12.8%
o 26
10.7%
n 17
 
7.0%
r 14
 
5.8%
e 14
 
5.8%
t 14
 
5.8%
p 13
 
5.4%
S 9
 
3.7%
k 9
 
3.7%
Other values (8) 58
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 242
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 37
15.3%
m 31
12.8%
o 26
10.7%
n 17
 
7.0%
r 14
 
5.8%
e 14
 
5.8%
t 14
 
5.8%
p 13
 
5.4%
S 9
 
3.7%
k 9
 
3.7%
Other values (8) 58
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 242
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 37
15.3%
m 31
12.8%
o 26
10.7%
n 17
 
7.0%
r 14
 
5.8%
e 14
 
5.8%
t 14
 
5.8%
p 13
 
5.4%
S 9
 
3.7%
k 9
 
3.7%
Other values (8) 58
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 242
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 37
15.3%
m 31
12.8%
o 26
10.7%
n 17
 
7.0%
r 14
 
5.8%
e 14
 
5.8%
t 14
 
5.8%
p 13
 
5.4%
S 9
 
3.7%
k 9
 
3.7%
Other values (8) 58
24.0%

season
Categorical

Constant 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
First warm / stratified
30 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters690
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFirst warm / stratified
2nd rowFirst warm / stratified
3rd rowFirst warm / stratified
4th rowFirst warm / stratified
5th rowFirst warm / stratified

Common Values

ValueCountFrequency (%)
First warm / stratified 30
100.0%

Length

2025-07-31T12:44:50.159167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-31T12:44:50.243575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
first 30
25.0%
warm 30
25.0%
30
25.0%
stratified 30
25.0%

Most occurring characters

ValueCountFrequency (%)
i 90
13.0%
t 90
13.0%
r 90
13.0%
90
13.0%
s 60
8.7%
a 60
8.7%
F 30
 
4.3%
w 30
 
4.3%
m 30
 
4.3%
/ 30
 
4.3%
Other values (3) 90
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 90
13.0%
t 90
13.0%
r 90
13.0%
90
13.0%
s 60
8.7%
a 60
8.7%
F 30
 
4.3%
w 30
 
4.3%
m 30
 
4.3%
/ 30
 
4.3%
Other values (3) 90
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 90
13.0%
t 90
13.0%
r 90
13.0%
90
13.0%
s 60
8.7%
a 60
8.7%
F 30
 
4.3%
w 30
 
4.3%
m 30
 
4.3%
/ 30
 
4.3%
Other values (3) 90
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 90
13.0%
t 90
13.0%
r 90
13.0%
90
13.0%
s 60
8.7%
a 60
8.7%
F 30
 
4.3%
w 30
 
4.3%
m 30
 
4.3%
/ 30
 
4.3%
Other values (3) 90
13.0%

depth_m
Real number (ℝ)

High correlation  Unique 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.660616
Minimum0.94449457
Maximum73.834
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:50.355646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.94449457
5-th percentile1.0437333
Q11.3023925
median13.358342
Q345.557183
95-th percentile71.980087
Maximum73.834
Range72.889505
Interquartile range (IQR)44.254791

Descriptive statistics

Standard deviation27.76378
Coefficient of variation (CV)1.0819608
Kurtosis-1.0831222
Mean25.660616
Median Absolute Deviation (MAD)12.308857
Skewness0.7104535
Sum769.81848
Variance770.82748
MonotonicityNot monotonic
2025-07-31T12:44:50.502418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1.180230222 1
 
3.3%
4.099514346 1
 
3.3%
29.11788745 1
 
3.3%
46.36342502 1
 
3.3%
0.9919696949 1
 
3.3%
72.93118429 1
 
3.3%
1.365841481 1
 
3.3%
13.82768423 1
 
3.3%
32.58135565 1
 
3.3%
2.197963414 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0.9444945722 1
3.3%
0.9919696949 1
3.3%
1.107 1
3.3%
1.134901015 1
3.3%
1.180230222 1
3.3%
1.197907387 1
3.3%
1.2418 1
3.3%
1.281242871 1
3.3%
1.365841481 1
3.3%
1.648 1
3.3%
ValueCountFrequency (%)
73.834 1
3.3%
72.93118429 1
3.3%
70.81763376 1
3.3%
70.69270421 1
3.3%
70.21100534 1
3.3%
70.0879 1
3.3%
47.293 1
3.3%
46.36342502 1
3.3%
43.13845815 1
3.3%
38.64216638 1
3.3%

depth_desc
Categorical

High correlation 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
surface
12 
dcm
12 
bdcm

Length

Max length7
Median length4
Mean length4.8
Min length3

Characters and Unicode

Total characters144
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsurface
2nd rowdcm
3rd rowbdcm
4th rowbdcm
5th rowsurface

Common Values

ValueCountFrequency (%)
surface 12
40.0%
dcm 12
40.0%
bdcm 6
20.0%

Length

2025-07-31T12:44:50.661051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-31T12:44:50.753156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
surface 12
40.0%
dcm 12
40.0%
bdcm 6
20.0%

Most occurring characters

ValueCountFrequency (%)
c 30
20.8%
d 18
12.5%
m 18
12.5%
s 12
 
8.3%
u 12
 
8.3%
r 12
 
8.3%
a 12
 
8.3%
f 12
 
8.3%
e 12
 
8.3%
b 6
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 144
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 30
20.8%
d 18
12.5%
m 18
12.5%
s 12
 
8.3%
u 12
 
8.3%
r 12
 
8.3%
a 12
 
8.3%
f 12
 
8.3%
e 12
 
8.3%
b 6
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 144
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 30
20.8%
d 18
12.5%
m 18
12.5%
s 12
 
8.3%
u 12
 
8.3%
r 12
 
8.3%
a 12
 
8.3%
f 12
 
8.3%
e 12
 
8.3%
b 6
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 144
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 30
20.8%
d 18
12.5%
m 18
12.5%
s 12
 
8.3%
u 12
 
8.3%
r 12
 
8.3%
a 12
 
8.3%
f 12
 
8.3%
e 12
 
8.3%
b 6
 
4.2%

sample_id
Text

Unique 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2025-07-31T12:44:50.979814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length23
Mean length22.533333
Min length22

Characters and Unicode

Total characters676
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st rowP4504-S1-250423-D1m-OA
2nd rowP4504-S1-250423-D4m-OA
3rd rowP4504-S1-250423-D30m-OA
4th rowP4504-S1-250423-D50m-OA
5th rowP4504-S2-250423-D1m-OA
ValueCountFrequency (%)
p4504-s1-250423-d1m-oa 1
 
3.3%
p4504-s1-250423-d4m-oa 1
 
3.3%
p4504-s1-250423-d30m-oa 1
 
3.3%
p4504-s1-250423-d50m-oa 1
 
3.3%
p4504-s2-250423-d1m-oa 1
 
3.3%
p4504-s2-250423-d79m-oa 1
 
3.3%
p4504-s3-250423-d1m-oa 1
 
3.3%
p4504-s3-250423-d10m-oa 1
 
3.3%
p4504-s3-250423-d30m-oa 1
 
3.3%
p4504-j1-250425-d1m-oa 1
 
3.3%
Other values (20) 20
66.7%
2025-07-31T12:44:51.275764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 120
17.8%
4 91
13.5%
5 72
10.7%
2 71
10.5%
0 67
9.9%
P 37
 
5.5%
A 36
 
5.3%
D 30
 
4.4%
m 30
 
4.4%
O 30
 
4.4%
Other values (8) 92
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 676
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 120
17.8%
4 91
13.5%
5 72
10.7%
2 71
10.5%
0 67
9.9%
P 37
 
5.5%
A 36
 
5.3%
D 30
 
4.4%
m 30
 
4.4%
O 30
 
4.4%
Other values (8) 92
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 676
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 120
17.8%
4 91
13.5%
5 72
10.7%
2 71
10.5%
0 67
9.9%
P 37
 
5.5%
A 36
 
5.3%
D 30
 
4.4%
m 30
 
4.4%
O 30
 
4.4%
Other values (8) 92
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 676
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 120
17.8%
4 91
13.5%
5 72
10.7%
2 71
10.5%
0 67
9.9%
P 37
 
5.5%
A 36
 
5.3%
D 30
 
4.4%
m 30
 
4.4%
O 30
 
4.4%
Other values (8) 92
13.6%

temp_wat
Real number (ℝ)

High correlation  Unique 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.215818
Minimum19.082805
Maximum29.806923
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:51.375294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19.082805
5-th percentile19.54102
Q123.714555
median28.832132
Q329.20983
95-th percentile29.558796
Maximum29.806923
Range10.724119
Interquartile range (IQR)5.495275

Descriptive statistics

Standard deviation3.9048894
Coefficient of variation (CV)0.14895165
Kurtosis-1.0093003
Mean26.215818
Median Absolute Deviation (MAD)0.72589809
Skewness-0.81308673
Sum786.47454
Variance15.248161
MonotonicityNot monotonic
2025-07-31T12:44:51.483755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
28.81119499 1
 
3.3%
28.85306824 1
 
3.3%
24.69285083 1
 
3.3%
21.61520446 1
 
3.3%
29.17469223 1
 
3.3%
20.24572079 1
 
3.3%
29.22154234 1
 
3.3%
29.26361535 1
 
3.3%
26.66851164 1
 
3.3%
28.93552243 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
19.08280485 1
3.3%
19.3515 1
3.3%
19.7726548 1
3.3%
19.8501 1
3.3%
20.14834999 1
3.3%
20.24572079 1
3.3%
21.61520446 1
3.3%
23.5993 1
3.3%
24.06031913 1
3.3%
24.69285083 1
3.3%
ValueCountFrequency (%)
29.8069235 1
3.3%
29.56568806 1
3.3%
29.55037135 1
3.3%
29.4828 1
3.3%
29.4549 1
3.3%
29.40581296 1
3.3%
29.26361535 1
3.3%
29.22154234 1
3.3%
29.17469223 1
3.3%
29.112 1
3.3%

temp_in_lb
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.823333
Minimum26.5
Maximum29.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:51.577758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26.5
5-th percentile26.5
Q127.125
median27.6
Q328.625
95-th percentile29.655
Maximum29.7
Range3.2
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.0526431
Coefficient of variation (CV)0.037833105
Kurtosis-0.91775139
Mean27.823333
Median Absolute Deviation (MAD)0.65
Skewness0.55081144
Sum834.7
Variance1.1080575
MonotonicityNot monotonic
2025-07-31T12:44:51.669271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
26.5 3
 
10.0%
27.2 3
 
10.0%
28 2
 
6.7%
27.9 2
 
6.7%
26.8 2
 
6.7%
26.6 2
 
6.7%
29.7 2
 
6.7%
27.4 2
 
6.7%
27.8 1
 
3.3%
28.1 1
 
3.3%
Other values (10) 10
33.3%
ValueCountFrequency (%)
26.5 3
10.0%
26.6 2
6.7%
26.8 2
6.7%
27.1 1
 
3.3%
27.2 3
10.0%
27.3 1
 
3.3%
27.4 2
6.7%
27.5 1
 
3.3%
27.7 1
 
3.3%
27.8 1
 
3.3%
ValueCountFrequency (%)
29.7 2
6.7%
29.6 1
3.3%
29.5 1
3.3%
29.3 1
3.3%
29.2 1
3.3%
28.9 1
3.3%
28.8 1
3.3%
28.1 1
3.3%
28 2
6.7%
27.9 2
6.7%

sal_wat
Real number (ℝ)

High correlation  Unique 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.516536
Minimum35.247255
Maximum35.953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:51.764933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum35.247255
5-th percentile35.288043
Q135.372545
median35.43922
Q335.686458
95-th percentile35.853508
Maximum35.953
Range0.70574549
Interquartile range (IQR)0.31391309

Descriptive statistics

Standard deviation0.20601051
Coefficient of variation (CV)0.005800411
Kurtosis-0.78062717
Mean35.516536
Median Absolute Deviation (MAD)0.12651702
Skewness0.69512128
Sum1065.4961
Variance0.042440329
MonotonicityNot monotonic
2025-07-31T12:44:51.893520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
35.42141543 1
 
3.3%
35.37781875 1
 
3.3%
35.60713262 1
 
3.3%
35.83779808 1
 
3.3%
35.42660915 1
 
3.3%
35.86636186 1
 
3.3%
35.48530922 1
 
3.3%
35.4719243 1
 
3.3%
35.33344679 1
 
3.3%
35.29367918 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
35.24725451 1
3.3%
35.28485096 1
3.3%
35.2919453 1
3.3%
35.29367918 1
3.3%
35.30068443 1
3.3%
35.31873575 1
3.3%
35.33344679 1
3.3%
35.37078717 1
3.3%
35.37781875 1
3.3%
35.3996 1
3.3%
ValueCountFrequency (%)
35.953 1
3.3%
35.86636186 1
3.3%
35.83779808 1
3.3%
35.82939726 1
3.3%
35.8164 1
3.3%
35.79796807 1
3.3%
35.71711571 1
3.3%
35.7129 1
3.3%
35.60713262 1
3.3%
35.57176979 1
3.3%

pres
Real number (ℝ)

High correlation  Unique 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.004355
Minimum11.08476
Maximum84.574
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:52.021552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11.08476
5-th percentile11.184994
Q111.4456
median23.600935
Q356.06424
95-th percentile82.704385
Maximum84.574
Range73.48924
Interquartile range (IQR)44.61864

Descriptive statistics

Standard deviation27.991868
Coefficient of variation (CV)0.77745785
Kurtosis-1.0831075
Mean36.004355
Median Absolute Deviation (MAD)12.410123
Skewness0.71046402
Sum1080.1306
Variance783.54469
MonotonicityNot monotonic
2025-07-31T12:44:52.139483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
11.32243342 1
 
3.3%
14.26571902 1
 
3.3%
39.48978567 1
 
3.3%
56.87711089 1
 
3.3%
11.13262507 1
 
3.3%
83.6633023 1
 
3.3%
11.50957072 1
 
3.3%
24.07387099 1
 
3.3%
42.98172948 1
 
3.3%
12.3485341 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
11.08475962 1
3.3%
11.13262507 1
3.3%
11.249 1
3.3%
11.27673153 1
3.3%
11.32243342 1
3.3%
11.34025592 1
3.3%
11.39 1
3.3%
11.42427656 1
3.3%
11.50957072 1
3.3%
11.7949 1
3.3%
ValueCountFrequency (%)
84.574 1
3.3%
83.6633023 1
3.3%
81.5323748 1
3.3%
81.40641811 1
3.3%
80.92075885 1
3.3%
80.7967 1
3.3%
57.8145 1
3.3%
56.87711089 1
3.3%
53.62562931 1
3.3%
49.09237038 1
3.3%

ph_lb
Real number (ℝ)

High correlation  Unique 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8731416
Minimum7.7497021
Maximum7.9759591
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:52.253867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7.7497021
5-th percentile7.780755
Q17.81325
median7.8837529
Q37.9248731
95-th percentile7.9592224
Maximum7.9759591
Range0.22625703
Interquartile range (IQR)0.11162309

Descriptive statistics

Standard deviation0.065343203
Coefficient of variation (CV)0.0082995082
Kurtosis-1.2276733
Mean7.8731416
Median Absolute Deviation (MAD)0.059252862
Skewness-0.22109048
Sum236.19425
Variance0.0042697341
MonotonicityNot monotonic
2025-07-31T12:44:52.376963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
7.911839638 1
 
3.3%
7.928872323 1
 
3.3%
7.79090425 1
 
3.3%
7.872870098 1
 
3.3%
7.94878005 1
 
3.3%
7.74970208 1
 
3.3%
7.957955633 1
 
3.3%
7.95028485 1
 
3.3%
7.874043144 1
 
3.3%
7.883 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
7.74970208 1
3.3%
7.780554619 1
3.3%
7.781 1
3.3%
7.785424712 1
3.3%
7.79090425 1
3.3%
7.801 1
3.3%
7.807 1
3.3%
7.81 1
3.3%
7.823 1
3.3%
7.826 1
3.3%
ValueCountFrequency (%)
7.975959107 1
3.3%
7.960258911 1
3.3%
7.957955633 1
3.3%
7.95028485 1
3.3%
7.94878005 1
3.3%
7.94517962 1
3.3%
7.928872323 1
3.3%
7.926968045 1
3.3%
7.918588215 1
3.3%
7.91758607 1
3.3%

ta
Real number (ℝ)

High correlation  Unique 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2228.6212
Minimum2057.4776
Maximum2389.2524
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:52.505872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2057.4776
5-th percentile2111.6995
Q12207.0179
median2239.2532
Q32262.8758
95-th percentile2325.6911
Maximum2389.2524
Range331.77475
Interquartile range (IQR)55.857965

Descriptive statistics

Standard deviation68.511459
Coefficient of variation (CV)0.030741634
Kurtosis1.0112988
Mean2228.6212
Median Absolute Deviation (MAD)31.581535
Skewness-0.36622378
Sum66858.637
Variance4693.8201
MonotonicityNot monotonic
2025-07-31T12:44:52.633791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2057.47762 1
 
3.3%
2186.362778 1
 
3.3%
2253.774596 1
 
3.3%
2277.409866 1
 
3.3%
2249.848174 1
 
3.3%
2284.362079 1
 
3.3%
2255.001536 1
 
3.3%
2271.934981 1
 
3.3%
2286.703113 1
 
3.3%
2125.023323 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
2057.47762 1
3.3%
2103.437173 1
3.3%
2121.797843 1
3.3%
2125.023323 1
3.3%
2156.538388 1
3.3%
2186.362778 1
3.3%
2193.909517 1
3.3%
2206.433172 1
3.3%
2208.771911 1
3.3%
2209.568564 1
3.3%
ValueCountFrequency (%)
2389.252375 1
3.3%
2332.38387 1
3.3%
2317.51098 1
3.3%
2286.703113 1
3.3%
2284.362079 1
3.3%
2277.409866 1
3.3%
2271.934981 1
3.3%
2265.500584 1
3.3%
2255.001536 1
3.3%
2253.774596 1
3.3%

ph
Real number (ℝ)

High correlation  Unique 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8956541
Minimum7.7922482
Maximum7.962521
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:52.753178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7.7922482
5-th percentile7.8272901
Q17.877509
median7.9019515
Q37.9258902
95-th percentile7.9531913
Maximum7.962521
Range0.17027281
Interquartile range (IQR)0.048381152

Descriptive statistics

Standard deviation0.041893226
Coefficient of variation (CV)0.0053058588
Kurtosis-0.051461152
Mean7.8956541
Median Absolute Deviation (MAD)0.024548191
Skewness-0.64415133
Sum236.86962
Variance0.0017550424
MonotonicityNot monotonic
2025-07-31T12:44:52.875667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
7.901378579 1
 
3.3%
7.916188532 1
 
3.3%
7.836247248 1
 
3.3%
7.962520994 1
 
3.3%
7.919639582 1
 
3.3%
7.850527111 1
 
3.3%
7.928071665 1
 
3.3%
7.920860351 1
 
3.3%
7.880692461 1
 
3.3%
7.880946156 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
7.792248189 1
3.3%
7.826791623 1
3.3%
7.827899309 1
3.3%
7.836247248 1
3.3%
7.837995195 1
3.3%
7.850527111 1
3.3%
7.874831467 1
3.3%
7.877004878 1
3.3%
7.879021359 1
3.3%
7.880692461 1
3.3%
ValueCountFrequency (%)
7.962520994 1
3.3%
7.957302329 1
3.3%
7.948166631 1
3.3%
7.940366958 1
3.3%
7.932205272 1
3.3%
7.928071665 1
3.3%
7.927001279 1
3.3%
7.926101259 1
3.3%
7.925256823 1
3.3%
7.920860351 1
3.3%

tc
Real number (ℝ)

High correlation  Unique 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1998.8665
Minimum1819.9409
Maximum2165.5951
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:53.085139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1819.9409
5-th percentile1883.3194
Q11967.4232
median1999.0128
Q32041.9462
95-th percentile2113.7496
Maximum2165.5951
Range345.65419
Interquartile range (IQR)74.523003

Descriptive statistics

Standard deviation73.222708
Coefficient of variation (CV)0.036632116
Kurtosis0.67674348
Mean1998.8665
Median Absolute Deviation (MAD)39.415467
Skewness-0.16157574
Sum59965.994
Variance5361.565
MonotonicityNot monotonic
2025-07-31T12:44:53.213749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1819.940887 1
 
3.3%
1930.992613 1
 
3.3%
2062.390786 1
 
3.3%
2043.43835 1
 
3.3%
1984.672221 1
 
3.3%
2112.186244 1
 
3.3%
1983.909706 1
 
3.3%
2003.006382 1
 
3.3%
2059.12809 1
 
3.3%
1892.612862 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1819.940887 1
3.3%
1875.715735 1
3.3%
1892.612862 1
3.3%
1894.138492 1
3.3%
1930.992613 1
3.3%
1949.462602 1
3.3%
1958.638895 1
3.3%
1966.442796 1
3.3%
1970.364481 1
3.3%
1980.892834 1
3.3%
ValueCountFrequency (%)
2165.595076 1
3.3%
2115.028697 1
3.3%
2112.186244 1
3.3%
2062.390786 1
3.3%
2061.538598 1
3.3%
2059.12809 1
3.3%
2058.881358 1
3.3%
2043.43835 1
3.3%
2037.469829 1
3.3%
2036.00011 1
3.3%

co2
Real number (ℝ)

High correlation  Unique 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.953109
Minimum13.453219
Maximum21.148512
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:53.559858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13.453219
5-th percentile13.507395
Q113.987352
median15.350051
Q317.375115
95-th percentile19.372153
Maximum21.148512
Range7.6952929
Interquartile range (IQR)3.3877632

Descriptive statistics

Standard deviation2.1921392
Coefficient of variation (CV)0.13741141
Kurtosis-0.54482031
Mean15.953109
Median Absolute Deviation (MAD)1.4758618
Skewness0.73047792
Sum478.59328
Variance4.8054743
MonotonicityNot monotonic
2025-07-31T12:44:53.662962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
13.45321863 1
 
3.3%
13.74590661 1
 
3.3%
19.46747715 1
 
3.3%
15.11027282 1
 
3.3%
13.90885361 1
 
3.3%
21.14851156 1
 
3.3%
13.59076851 1
 
3.3%
13.94524883 1
 
3.3%
16.736006 1
 
3.3%
14.69881789 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
13.45321863 1
3.3%
13.45933809 1
3.3%
13.56613093 1
3.3%
13.59076851 1
3.3%
13.74590661 1
3.3%
13.75656437 1
3.3%
13.90885361 1
3.3%
13.94524883 1
3.3%
14.11366151 1
3.3%
14.68132795 1
3.3%
ValueCountFrequency (%)
21.14851156 1
3.3%
19.46747715 1
3.3%
19.25564472 1
3.3%
19.17316241 1
3.3%
18.81569134 1
3.3%
18.58572405 1
3.3%
18.57574507 1
3.3%
17.54662786 1
3.3%
16.86057717 1
3.3%
16.736006 1
3.3%

hco3-
Real number (ℝ)

High correlation  Unique 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1818.2596
Minimum1642.3065
Maximum1978.8966
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:53.769683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1642.3065
5-th percentile1701.0612
Q11778.5747
median1814.725
Q31866.7729
95-th percentile1952.3086
Maximum1978.8966
Range336.59006
Interquartile range (IQR)88.198119

Descriptive statistics

Standard deviation78.709526
Coefficient of variation (CV)0.043288387
Kurtosis-0.024614423
Mean1818.2596
Median Absolute Deviation (MAD)44.368202
Skewness0.059387199
Sum54547.789
Variance6195.1894
MonotonicityNot monotonic
2025-07-31T12:44:53.872964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1642.30655 1
 
3.3%
1737.405111 1
 
3.3%
1901.25088 1
 
3.3%
1861.704225 1
 
3.3%
1782.448509 1
 
3.3%
1961.820233 1
 
3.3%
1778.187134 1
 
3.3%
1797.636679 1
 
3.3%
1876.847675 1
 
3.3%
1714.156037 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1642.30655 1
3.3%
1690.347249 1
3.3%
1714.156037 1
3.3%
1728.152239 1
3.3%
1737.405111 1
3.3%
1748.857944 1
3.3%
1772.96782 1
3.3%
1778.187134 1
3.3%
1779.737548 1
3.3%
1782.448509 1
3.3%
ValueCountFrequency (%)
1978.896609 1
3.3%
1961.820233 1
3.3%
1940.683337 1
3.3%
1907.251764 1
3.3%
1906.652795 1
3.3%
1901.25088 1
3.3%
1876.847675 1
3.3%
1868.4624 1
3.3%
1861.704225 1
3.3%
1852.141875 1
3.3%

co3-
Real number (ℝ)

High correlation  Unique 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.65375
Minimum129.2175
Maximum200.22391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:53.982145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum129.2175
5-th percentile134.03722
Q1148.2252
median166.08413
Q3179.90382
95-th percentile191.8135
Maximum200.22391
Range71.006415
Interquartile range (IQR)31.678616

Descriptive statistics

Standard deviation19.312248
Coefficient of variation (CV)0.11729006
Kurtosis-0.93186388
Mean164.65375
Median Absolute Deviation (MAD)16.698901
Skewness-0.096363235
Sum4939.6125
Variance372.96291
MonotonicityNot monotonic
2025-07-31T12:44:54.114122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
164.181119 1
 
3.3%
179.8415952 1
 
3.3%
141.6724289 1
 
3.3%
166.623853 1
 
3.3%
188.314858 1
 
3.3%
129.2174994 1
 
3.3%
192.1318033 1
 
3.3%
191.4244541 1
 
3.3%
165.5444092 1
 
3.3%
163.7580074 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
129.2174994 1
3.3%
132.3739497 1
3.3%
136.070111 1
3.3%
141.6724289 1
3.3%
142.8757128 1
3.3%
144.5408461 1
3.3%
146.7917266 1
3.3%
147.8247883 1
3.3%
149.4264462 1
3.3%
149.9910817 1
3.3%
ValueCountFrequency (%)
200.2239146 1
3.3%
192.1318033 1
3.3%
191.4244541 1
3.3%
188.314858 1
3.3%
187.1453209 1
3.3%
183.2829997 1
3.3%
182.824248 1
3.3%
179.9245595 1
3.3%
179.8415952 1
3.3%
174.3397375 1
3.3%

omega_ca
Real number (ℝ)

High correlation  Unique 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9470972
Minimum3.0363243
Maximum4.842224
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:54.239057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.0363243
5-th percentile3.146947
Q13.5169156
median3.965894
Q34.3546323
95-th percentile4.6301242
Maximum4.842224
Range1.8058997
Interquartile range (IQR)0.83771675

Descriptive statistics

Standard deviation0.49423647
Coefficient of variation (CV)0.12521518
Kurtosis-0.94924777
Mean3.9470972
Median Absolute Deviation (MAD)0.42567308
Skewness-0.1197587
Sum118.41291
Variance0.24426969
MonotonicityNot monotonic
2025-07-31T12:44:54.363839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3.964702834 1
 
3.3%
4.343258661 1
 
3.3%
3.377475798 1
 
3.3%
3.938284403 1
 
3.3%
4.550803623 1
 
3.3%
3.036324303 1
 
3.3%
4.640803219 1
 
3.3%
4.617071998 1
 
3.3%
3.967085254 1
 
3.3%
3.959496501 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3.036324303 1
3.3%
3.105161133 1
3.3%
3.198018566 1
3.3%
3.377475798 1
3.3%
3.411037574 1
3.3%
3.448217862 1
3.3%
3.478304772 1
3.3%
3.499472375 1
3.3%
3.569245201 1
3.3%
3.618573802 1
3.3%
ValueCountFrequency (%)
4.842223961 1
3.3%
4.640803219 1
3.3%
4.617071998 1
3.3%
4.550803623 1
3.3%
4.521580063 1
3.3%
4.421086883 1
3.3%
4.420591368 1
3.3%
4.358423561 1
3.3%
4.343258661 1
3.3%
4.215885908 1
3.3%

omega_ar
Real number (ℝ)

High correlation  Unique 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6186299
Minimum1.9787497
Maximum3.2397846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:54.477042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.9787497
5-th percentile2.0471632
Q12.3452995
median2.6455442
Q32.9122129
95-th percentile3.0961935
Maximum3.2397846
Range1.2610348
Interquartile range (IQR)0.56691337

Descriptive statistics

Standard deviation0.34889944
Coefficient of variation (CV)0.1332374
Kurtosis-0.97600046
Mean2.6186299
Median Absolute Deviation (MAD)0.29900126
Skewness-0.1382758
Sum78.558898
Variance0.12173082
MonotonicityNot monotonic
2025-07-31T12:44:54.634277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.646991535 1
 
3.3%
2.900027267 1
 
3.3%
2.226975617 1
 
3.3%
2.575153946 1
 
3.3%
3.042019186 1
 
3.3%
1.978749713 1
 
3.3%
3.102973357 1
 
3.3%
3.087906999 1
 
3.3%
2.630753024 1
 
3.3%
2.644096936 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1.978749713 1
3.3%
2.019163132 1
3.3%
2.081385482 1
3.3%
2.226975617 1
3.3%
2.240182663 1
3.3%
2.254954556 1
3.3%
2.265705 1
3.3%
2.344056017 1
3.3%
2.349029935 1
3.3%
2.409869875 1
3.3%
ValueCountFrequency (%)
3.239784551 1
3.3%
3.102973357 1
3.3%
3.087906999 1
3.3%
3.042019186 1
3.3%
3.021886672 1
3.3%
2.954463148 1
3.3%
2.95359144 1
3.3%
2.916274735 1
3.3%
2.900027267 1
3.3%
2.820245505 1
3.3%

revelle
Real number (ℝ)

High correlation  Unique 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.78115
Minimum9.866457
Maximum12.63459
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:54.777818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.866457
5-th percentile9.867088
Q110.013786
median10.671498
Q311.354385
95-th percentile12.184487
Maximum12.63459
Range2.7681333
Interquartile range (IQR)1.3405986

Descriptive statistics

Standard deviation0.80040755
Coefficient of variation (CV)0.074241392
Kurtosis-0.58740837
Mean10.78115
Median Absolute Deviation (MAD)0.67322825
Skewness0.63069302
Sum323.43449
Variance0.64065224
MonotonicityNot monotonic
2025-07-31T12:44:54.903518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
9.985909424 1
 
3.3%
9.99315893 1
 
3.3%
11.83471107 1
 
3.3%
10.85462911 1
 
3.3%
9.96869655 1
 
3.3%
12.63459035 1
 
3.3%
9.86645704 1
 
3.3%
9.953597341 1
 
3.3%
10.94380893 1
 
3.3%
10.30595859 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
9.86645704 1
3.3%
9.866871567 1
3.3%
9.867352538 1
3.3%
9.953597341 1
3.3%
9.96869655 1
3.3%
9.985909424 1
3.3%
9.99315893 1
3.3%
10.00338121 1
3.3%
10.04500007 1
3.3%
10.18277905 1
3.3%
ValueCountFrequency (%)
12.63459035 1
3.3%
12.25090346 1
3.3%
12.10331145 1
3.3%
11.83471107 1
3.3%
11.57076513 1
3.3%
11.47234846 1
3.3%
11.40723528 1
3.3%
11.36157049 1
3.3%
11.33282671 1
3.3%
11.26335514 1
3.3%

nitrate_nitrite
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5738336
Minimum0.0042309916
Maximum10.975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:55.017887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0042309916
5-th percentile0.0083395466
Q10.020251798
median0.041140568
Q30.93099343
95-th percentile8.101
Maximum10.975
Range10.970769
Interquartile range (IQR)0.91074163

Descriptive statistics

Standard deviation3.046069
Coefficient of variation (CV)1.9354454
Kurtosis2.8571059
Mean1.5738336
Median Absolute Deviation (MAD)0.031140568
Skewness1.9816659
Sum47.215009
Variance9.2785364
MonotonicityNot monotonic
2025-07-31T12:44:55.138673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.01 2
 
6.7%
0.335 1
 
3.3%
0.1458676696 1
 
3.3%
0.004230991618 1
 
3.3%
0.07 1
 
3.3%
8.09 1
 
3.3%
0.006980993759 1
 
3.3%
0.02945087751 1
 
3.3%
0.045 1
 
3.3%
0.05007438576 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.004230991618 1
3.3%
0.006980993759 1
3.3%
0.01 2
6.7%
0.01199437932 1
3.3%
0.01443619695 1
3.3%
0.01687463199 1
3.3%
0.0193236776 1
3.3%
0.02303615986 1
3.3%
0.02554562867 1
3.3%
0.02808758098 1
3.3%
ValueCountFrequency (%)
10.975 1
3.3%
8.11 1
3.3%
8.09 1
3.3%
6.975 1
3.3%
5.12 1
3.3%
3.085586107 1
3.3%
2.415 1
3.3%
1.129657909 1
3.3%
0.335 1
3.3%
0.2226340068 1
3.3%

ammonium
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.066273238
Minimum0.0029961384
Maximum1.245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:55.260593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0029961384
5-th percentile0.0047498992
Q10.0078368495
median0.011758822
Q30.024509042
95-th percentile0.14389309
Maximum1.245
Range1.2420039
Interquartile range (IQR)0.016672192

Descriptive statistics

Standard deviation0.22565482
Coefficient of variation (CV)3.4049162
Kurtosis28.250372
Mean0.066273238
Median Absolute Deviation (MAD)0.0046992287
Skewness5.2563848
Sum1.9881971
Variance0.050920099
MonotonicityNot monotonic
2025-07-31T12:44:55.395757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.005 2
 
6.7%
0.002996138444 1
 
3.3%
0.004545271208 1
 
3.3%
0.005543555317 1
 
3.3%
0.00635200274 1
 
3.3%
0.007055457849 1
 
3.3%
0.007690367584 1
 
3.3%
0.008276295348 1
 
3.3%
0.008825148188 1
 
3.3%
0.009344793786 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.002996138444 1
3.3%
0.004545271208 1
3.3%
0.005 2
6.7%
0.005543555317 1
3.3%
0.00635200274 1
3.3%
0.007055457849 1
3.3%
0.007690367584 1
3.3%
0.008276295348 1
3.3%
0.008825148188 1
3.3%
0.009344793786 1
3.3%
ValueCountFrequency (%)
1.245 1
3.3%
0.17 1
3.3%
0.1119846385 1
3.3%
0.0835 1
3.3%
0.06238875249 1
3.3%
0.04138104591 1
3.3%
0.03705620231 1
3.3%
0.02719408369 1
3.3%
0.01645391529 1
3.3%
0.01462942606 1
3.3%

phosphate
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.097038067
Minimum0.001864136
Maximum0.535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:55.519685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.001864136
5-th percentile0.0032404835
Q10.006042986
median0.0085680204
Q30.0775
95-th percentile0.51175
Maximum0.535
Range0.53313586
Interquartile range (IQR)0.071457014

Descriptive statistics

Standard deviation0.17318254
Coefficient of variation (CV)1.7846867
Kurtosis1.9069957
Mean0.097038067
Median Absolute Deviation (MAD)0.0046591563
Skewness1.847005
Sum2.911142
Variance0.029992193
MonotonicityNot monotonic
2025-07-31T12:44:55.661467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.01 2
 
6.7%
0.001864135978 1
 
3.3%
0.002926572508 1
 
3.3%
0.003624152524 1
 
3.3%
0.004193575612 1
 
3.3%
0.535 1
 
3.3%
0.004691421143 1
 
3.3%
0.00514219337 1
 
3.3%
0.005559121004 1
 
3.3%
0.0295 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.001864135978 1
3.3%
0.002926572508 1
3.3%
0.003624152524 1
3.3%
0.004193575612 1
3.3%
0.004691421143 1
3.3%
0.00514219337 1
3.3%
0.005559121004 1
3.3%
0.005950291659 1
3.3%
0.006321068947 1
3.3%
0.006675221485 1
3.3%
ValueCountFrequency (%)
0.535 1
3.3%
0.523 1
3.3%
0.498 1
3.3%
0.415 1
3.3%
0.35 1
3.3%
0.1636316232 1
3.3%
0.100964518 1
3.3%
0.085 1
3.3%
0.055 1
3.3%
0.0295 1
3.3%

silicate
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4934667
Minimum0.1
Maximum3.068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:55.776952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4267
Q11.087875
median1.355
Q31.864625
95-th percentile2.745575
Maximum3.068
Range2.968
Interquartile range (IQR)0.77675

Descriptive statistics

Standard deviation0.70904676
Coefficient of variation (CV)0.47476571
Kurtosis0.22364223
Mean1.4934667
Median Absolute Deviation (MAD)0.30675
Skewness0.3749249
Sum44.804
Variance0.50274731
MonotonicityNot monotonic
2025-07-31T12:44:55.892280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.1 2
 
6.7%
1.028 1
 
3.3%
2.8205 1
 
3.3%
0.943 1
 
3.3%
0.85 1
 
3.3%
2.497 1
 
3.3%
2.2825 1
 
3.3%
1.279 1
 
3.3%
1.43 1
 
3.3%
3.068 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.1 2
6.7%
0.826 1
3.3%
0.85 1
3.3%
0.943 1
3.3%
1.0165 1
3.3%
1.028 1
3.3%
1.0805 1
3.3%
1.11 1
3.3%
1.256 1
3.3%
1.2605 1
3.3%
ValueCountFrequency (%)
3.068 1
3.3%
2.8205 1
3.3%
2.654 1
3.3%
2.497 1
3.3%
2.2825 1
3.3%
2.2785 1
3.3%
2.137 1
3.3%
1.939 1
3.3%
1.6415 1
3.3%
1.637 1
3.3%

chl
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30326923
Minimum0.175
Maximum0.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:56.003281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.175
5-th percentile0.18675
Q10.255
median0.2975
Q30.345
95-th percentile0.431
Maximum0.61
Range0.435
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.092493637
Coefficient of variation (CV)0.30498853
Kurtosis2.8345982
Mean0.30326923
Median Absolute Deviation (MAD)0.0475
Skewness1.281352
Sum9.0980769
Variance0.0085550729
MonotonicityNot monotonic
2025-07-31T12:44:56.102367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.3032692308 4
 
13.3%
0.275 2
 
6.7%
0.195 2
 
6.7%
0.255 2
 
6.7%
0.265 1
 
3.3%
0.28 1
 
3.3%
0.295 1
 
3.3%
0.44 1
 
3.3%
0.33 1
 
3.3%
0.3 1
 
3.3%
Other values (14) 14
46.7%
ValueCountFrequency (%)
0.175 1
3.3%
0.18 1
3.3%
0.195 2
6.7%
0.205 1
3.3%
0.225 1
3.3%
0.23 1
3.3%
0.255 2
6.7%
0.26 1
3.3%
0.265 1
3.3%
0.275 2
6.7%
ValueCountFrequency (%)
0.61 1
3.3%
0.44 1
3.3%
0.42 1
3.3%
0.415 1
3.3%
0.4 1
3.3%
0.385 1
3.3%
0.365 1
3.3%
0.35 1
3.3%
0.33 1
3.3%
0.305 1
3.3%

o2
Real number (ℝ)

High correlation 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1405333
Minimum0.028
Maximum9.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size372.0 B
2025-07-31T12:44:56.198654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.028
5-th percentile0.7066
Q12.4325
median4.175
Q36.0425
95-th percentile7.011
Maximum9.42
Range9.392
Interquartile range (IQR)3.61

Descriptive statistics

Standard deviation2.3112643
Coefficient of variation (CV)0.5582045
Kurtosis-0.57542843
Mean4.1405333
Median Absolute Deviation (MAD)1.86
Skewness0.085501993
Sum124.216
Variance5.3419429
MonotonicityNot monotonic
2025-07-31T12:44:56.311789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
6.02 2
 
6.7%
7 2
 
6.7%
4.02 1
 
3.3%
6.05 1
 
3.3%
3.02 1
 
3.3%
2.626 1
 
3.3%
1.026 1
 
3.3%
1.826 1
 
3.3%
7.02 1
 
3.3%
6.1 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0.028 1
3.3%
0.496 1
3.3%
0.964 1
3.3%
1.026 1
3.3%
1.432 1
3.3%
1.826 1
3.3%
1.9 1
3.3%
2.368 1
3.3%
2.626 1
3.3%
2.836 1
3.3%
ValueCountFrequency (%)
9.42 1
3.3%
7.02 1
3.3%
7 2
6.7%
6.23 1
3.3%
6.21 1
3.3%
6.1 1
3.3%
6.05 1
3.3%
6.02 2
6.7%
5.21 1
3.3%
5.02 1
3.3%

datetime
Date

Unique 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2025-04-23 13:23:00
Maximum2025-04-27 14:28:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-31T12:44:56.412381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:56.517778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)

cruise
Categorical

Constant 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
apr_25
30 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters180
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowapr_25
2nd rowapr_25
3rd rowapr_25
4th rowapr_25
5th rowapr_25

Common Values

ValueCountFrequency (%)
apr_25 30
100.0%

Length

2025-07-31T12:44:56.623949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-31T12:44:56.678994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
apr_25 30
100.0%

Most occurring characters

ValueCountFrequency (%)
a 30
16.7%
p 30
16.7%
r 30
16.7%
_ 30
16.7%
2 30
16.7%
5 30
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 30
16.7%
p 30
16.7%
r 30
16.7%
_ 30
16.7%
2 30
16.7%
5 30
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 30
16.7%
p 30
16.7%
r 30
16.7%
_ 30
16.7%
2 30
16.7%
5 30
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 30
16.7%
p 30
16.7%
r 30
16.7%
_ 30
16.7%
2 30
16.7%
5 30
16.7%

nitrate_nitrite_imputed
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
28 
True
 
2
ValueCountFrequency (%)
False 28
93.3%
True 2
 
6.7%
2025-07-31T12:44:56.716923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

ammonium_imputed
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
28 
True
 
2
ValueCountFrequency (%)
False 28
93.3%
True 2
 
6.7%
2025-07-31T12:44:56.759888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

phosphate_imputed
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
28 
True
 
2
ValueCountFrequency (%)
False 28
93.3%
True 2
 
6.7%
2025-07-31T12:44:56.802565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

silicate_imputed
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
28 
True
 
2
ValueCountFrequency (%)
False 28
93.3%
True 2
 
6.7%
2025-07-31T12:44:56.841789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

chl_imputed
Boolean

High correlation 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
26 
True
ValueCountFrequency (%)
False 26
86.7%
True 4
 
13.3%
2025-07-31T12:44:56.888750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2025-07-31T12:44:45.708097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:43:53.874791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:43:56.459916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:43:58.891209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:01.456828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:04.058872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:06.409360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:08.695319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:10.998770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:13.080977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:15.212254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:17.434951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:19.563933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:22.215722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:24.469742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:27.095170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:29.641281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:33.056245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:35.373879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:37.324721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:39.649088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:41.624135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:43.808549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:45.781699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:43:53.973918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:43:56.545112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:43:58.991580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:01.551815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:04.161472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:06.488145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:08.784460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:11.075430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:13.160941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:15.292043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:17.510975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:19.645543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:22.306882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:24.577174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:27.198261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:29.742311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:33.140086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:35.450169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:37.404431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:39.727419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:41.697541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:43.878362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:45.863259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:43:54.091268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:43:56.635071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:43:59.099294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:01.647805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:04.266352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:06.575172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:08.887247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:11.161956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:13.255183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:15.379278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:17.605113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:19.736938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:22.405722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:24.710384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:27.309519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:29.839648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:33.240703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:35.539351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:37.489024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:39.809459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:41.786953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:43.958317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:45.940970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:43:54.195579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:43:56.731520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:43:59.214618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:01.752398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:04.375226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:06.664049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:08.980547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:11.256770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:13.342431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:15.469787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:17.692011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:19.838965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:22.511046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:24.823961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:27.413840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:29.941350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:33.341001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:35.624754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:37.580418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:39.891993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:41.876463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:44.040694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:46.024735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:43:54.300135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:43:56.822181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:43:59.334746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:01.862481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:04.503059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:06.750633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:09.072119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:11.339973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:13.432587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:15.557525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:17.780325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:19.973658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:22.604654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:24.925485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:27.515931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:30.069314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:33.432584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:35.711429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:37.665357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:39.974592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:41.961476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:44.115201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:46.105411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:43:54.410847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:43:56.922387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:43:59.455288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:01.971143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:04.647093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:06.835789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:09.164340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:11.427053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-31T12:44:37.248734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:39.571351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:41.542350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:43.729349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T12:44:45.637965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-31T12:44:56.997125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ammoniumammonium_imputedchlchl_imputedco2co3-depth_descdepth_mhco3-latloclonnitrate_nitritenitrate_nitrite_imputedo2omega_aromega_caphph_lbphosphatephosphate_imputedpresrevellesal_watsilicatesilicate_imputedstationtatctemp_in_lbtemp_wat
ammonium1.0000.000-0.2730.0000.248-0.0580.000-0.0210.1720.2090.0000.3730.3480.000-0.160-0.049-0.049-0.099-0.1000.5280.000-0.0210.175-0.0340.2100.0000.133-0.0550.057-0.1140.127
ammonium_imputed0.0001.0000.0000.0000.0000.0000.4740.4800.3990.0000.2560.0000.0000.7210.0000.0000.0000.0000.3990.0000.7210.4800.3990.0000.8660.7210.0000.2200.0000.0000.866
chl-0.2730.0001.0000.1590.325-0.3200.3950.6060.5280.0980.0000.3990.0480.0000.072-0.377-0.3430.206-0.2800.2280.0000.6060.4110.584-0.2680.0000.0000.3810.536-0.254-0.531
chl_imputed0.0000.0000.1591.0000.3570.0000.7520.2980.5390.0000.0000.0000.0000.0000.0000.0850.0850.0000.0000.0000.0000.2980.3190.3760.0000.0000.0000.0000.1000.0000.601
co20.2480.0000.3250.3571.000-0.8140.1950.5620.795-0.1720.205-0.0830.0650.0000.043-0.814-0.813-0.534-0.8840.4150.0000.5620.9390.3950.0350.0000.2260.2730.6780.140-0.432
co3--0.0580.000-0.3200.000-0.8141.0000.179-0.593-0.5180.1170.1540.238-0.2870.000-0.1780.9920.9950.3980.956-0.4290.000-0.593-0.911-0.465-0.1130.0000.2060.168-0.333-0.3300.623
depth_desc0.0000.4740.3950.7520.1950.1791.0000.6440.5920.0000.0000.0000.0000.4740.0000.0000.0000.2170.3370.0000.4740.6440.4590.4000.3830.4740.0000.0000.3960.0000.517
depth_m-0.0210.4800.6060.2980.562-0.5930.6441.0000.7140.0420.0000.1050.2000.480-0.014-0.664-0.6400.207-0.6070.5130.4801.0000.7220.7350.1790.4800.0000.2980.624-0.086-0.890
hco3-0.1720.3990.5280.5390.795-0.5180.5920.7141.000-0.0950.1660.1670.0670.399-0.091-0.571-0.547-0.087-0.6270.4560.3990.7140.7770.605-0.0790.3990.1920.6990.960-0.156-0.603
lat0.2090.0000.0980.000-0.1720.1170.0000.042-0.0951.0000.6980.5540.1380.0000.0710.1080.1010.1340.2090.0980.0000.042-0.0620.134-0.0530.0000.949-0.088-0.049-0.425-0.115
loc0.0000.2560.0000.0000.2050.1540.0000.0000.1660.6981.0000.8990.0660.2560.4220.1220.1220.0000.2530.2000.2560.0000.2330.2240.0000.2560.8320.3620.0000.7890.000
lon0.3730.0000.3990.000-0.0830.2380.0000.1050.1670.5540.8991.0000.3460.000-0.1560.2040.2300.3640.2990.4210.0000.105-0.0770.204-0.0390.0000.9260.3080.214-0.662-0.011
nitrate_nitrite0.3480.0000.0480.0000.065-0.2870.0000.2000.0670.1380.0660.3461.0000.000-0.101-0.288-0.2710.172-0.2000.6240.0000.2000.1990.2460.4190.0000.338-0.197-0.063-0.059-0.300
nitrate_nitrite_imputed0.0000.7210.0000.0000.0000.0000.4740.4800.3990.0000.2560.0000.0001.0000.0000.0000.0000.0000.3990.0000.7210.4800.3990.0000.8660.7210.0000.2200.0000.0000.866
o2-0.1600.0000.0720.0000.043-0.1780.000-0.014-0.0910.0710.422-0.156-0.1010.0001.000-0.172-0.162-0.287-0.223-0.1560.000-0.0140.134-0.392-0.3380.0000.572-0.279-0.1260.612-0.072
omega_ar-0.0490.000-0.3770.085-0.8140.9920.000-0.664-0.5710.1080.1220.204-0.2880.000-0.1721.0000.9960.3140.957-0.4780.000-0.664-0.931-0.530-0.0940.0000.0000.114-0.385-0.3050.694
omega_ca-0.0490.000-0.3430.085-0.8130.9950.000-0.640-0.5470.1010.1220.230-0.2710.000-0.1620.9961.0000.3470.958-0.4410.000-0.640-0.924-0.519-0.0960.0000.0000.143-0.361-0.3200.666
ph-0.0990.0000.2060.000-0.5340.3980.2170.207-0.0870.1340.0000.3640.1720.000-0.2870.3140.3471.0000.4600.1600.0000.207-0.3660.3660.0140.0000.0920.151-0.050-0.375-0.291
ph_lb-0.1000.399-0.2800.000-0.8840.9560.337-0.607-0.6270.2090.2530.299-0.2000.399-0.2230.9570.9580.4601.000-0.4330.399-0.607-0.946-0.410-0.1010.3990.2670.023-0.459-0.4280.594
phosphate0.5280.0000.2280.0000.415-0.4290.0000.5130.4560.0980.2000.4210.6240.000-0.156-0.478-0.4410.160-0.4331.0000.0000.5130.5090.3960.3010.0000.4790.1100.307-0.181-0.437
phosphate_imputed0.0000.7210.0000.0000.0000.0000.4740.4800.3990.0000.2560.0000.0000.7210.0000.0000.0000.0000.3990.0001.0000.4800.3990.0000.8660.7210.0000.2200.0000.0000.866
pres-0.0210.4800.6060.2980.562-0.5930.6441.0000.7140.0420.0000.1050.2000.480-0.014-0.664-0.6400.207-0.6070.5130.4801.0000.7220.7350.1790.4800.0000.2980.624-0.086-0.890
revelle0.1750.3990.4110.3190.939-0.9110.4590.7220.777-0.0620.233-0.0770.1990.3990.134-0.931-0.924-0.366-0.9460.5090.3990.7221.0000.5340.0460.3990.0680.1540.6260.186-0.669
sal_wat-0.0340.0000.5840.3760.395-0.4650.4000.7350.6050.1340.2240.2040.2460.000-0.392-0.530-0.5190.366-0.4100.3960.0000.7350.5341.0000.0280.0000.0000.3140.543-0.340-0.762
silicate0.2100.866-0.2680.0000.035-0.1130.3830.179-0.079-0.0530.000-0.0390.4190.866-0.338-0.094-0.0960.014-0.1010.3010.8660.1790.0460.0281.0000.8660.000-0.169-0.131-0.025-0.100
silicate_imputed0.0000.7210.0000.0000.0000.0000.4740.4800.3990.0000.2560.0000.0000.7210.0000.0000.0000.0000.3990.0000.7210.4800.3990.0000.8661.0000.0000.2200.0000.0000.866
station0.1330.0000.0000.0000.2260.2060.0000.0000.1920.9490.8320.9260.3380.0000.5720.0000.0000.0920.2670.4790.0000.0000.0680.0000.0000.0001.0000.0000.0000.6220.000
ta-0.0550.2200.3810.0000.2730.1680.0000.2980.699-0.0880.3620.308-0.1970.220-0.2790.1140.1430.1510.0230.1100.2200.2980.1540.314-0.1690.2200.0001.0000.842-0.450-0.165
tc0.0570.0000.5360.1000.678-0.3330.3960.6240.960-0.0490.0000.214-0.0630.000-0.126-0.385-0.361-0.050-0.4590.3070.0000.6240.6260.543-0.1310.0000.0000.8421.000-0.254-0.499
temp_in_lb-0.1140.000-0.2540.0000.140-0.3300.000-0.086-0.156-0.4250.789-0.662-0.0590.0000.612-0.305-0.320-0.375-0.428-0.1810.000-0.0860.186-0.340-0.0250.0000.622-0.450-0.2541.0000.011
temp_wat0.1270.866-0.5310.601-0.4320.6230.517-0.890-0.603-0.1150.000-0.011-0.3000.866-0.0720.6940.666-0.2910.594-0.4370.866-0.890-0.669-0.762-0.1000.8660.000-0.165-0.4990.0111.000

Missing values

2025-07-31T12:44:48.101619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-31T12:44:48.516853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

stationdatetimelatlonlocseasondepth_mdepth_descsample_idtemp_wattemp_in_lbsal_watpresph_lbtaphtcco2hco3-co3-omega_caomega_arrevellenitrate_nitriteammoniumphosphatesilicatechlo2datetimecruisenitrate_nitrite_imputedammonium_imputedphosphate_imputedsilicate_imputedchl_imputed
0S12025-04-2313:23:005.49-0.020SakumonoFirst warm / stratified1.180230surfaceP4504-S1-250423-D1m-OA28.81119528.135.42141511.3224337.9118402057.4776207.9013791819.94088713.4532191642.306550164.1811193.9647032.6469929.9859090.3350000.0029960.0018641.02800.2800006.0202025-04-23 13:23:00apr_25FalseFalseFalseFalseFalse
1S12025-04-2313:50:005.49-0.020SakumonoFirst warm / stratified4.099514dcmP4504-S1-250423-D4m-OA28.85306828.035.37781914.2657197.9288722186.3627787.9161891930.99261313.7459071737.405111179.8415954.3432592.9000279.9931590.1458680.0045450.0029272.82050.2950006.0202025-04-23 13:50:00apr_25FalseFalseFalseFalseFalse
2S12025-04-2314:32:005.49-0.020SakumonoFirst warm / stratified29.117887bdcmP4504-S1-250423-D30m-OA24.69285127.935.60713339.4897867.7909042253.7745967.8362472062.39078619.4674771901.250880141.6724293.3774762.22697611.8347110.0042310.0055440.0036240.94300.4400006.0502025-04-23 14:32:00apr_25FalseFalseFalseFalseFalse
3S12025-04-2314:15:005.49-0.020SakumonoFirst warm / stratified46.363425bdcmP4504-S1-250423-D50m-OA21.61520427.835.83779856.8771117.8728702277.4098667.9625212043.43835015.1102731861.704225166.6238533.9382842.57515410.8546290.0100000.0050000.0100000.10000.3032694.0202025-04-23 14:15:00apr_25TrueTrueTrueTrueTrue
4S22025-04-2316:25:005.400.025SakumonoFirst warm / stratified0.991970surfaceP4504-S2-250423-D1m-OA29.17469227.235.42660911.1326257.9487802249.8481747.9196401984.67222113.9088541782.448509188.3148584.5508043.0420199.9686970.0700000.0063520.0041940.85000.2650003.0202025-04-23 16:25:00apr_25FalseFalseFalseFalseFalse
5S22025-04-2316:24:005.400.025SakumonoFirst warm / stratified72.931184dcmP4504-S2-250423-D79m-OA20.24572127.435.86636283.6633027.7497022284.3620797.8505272112.18624421.1485121961.820233129.2174993.0363241.97875012.6345908.0900000.0070550.5350002.49700.3850002.6262025-04-23 16:24:00apr_25FalseFalseFalseFalseFalse
6S32025-04-2317:42:005.310.070SakumonoFirst warm / stratified1.365841surfaceP4504-S3-250423-D1m-OA29.22154227.235.48530911.5095717.9579562255.0015367.9280721983.90970613.5907691778.187134192.1318034.6408033.1029739.8664570.0069810.0076900.0046912.28250.3000001.8262025-04-23 17:42:00apr_25FalseFalseFalseFalseFalse
7S32025-04-2318:05:005.310.070SakumonoFirst warm / stratified13.827684dcmP4504-S3-250423-D10m-OA29.26361527.335.47192424.0738717.9502852271.9349817.9208602003.00638213.9452491797.636679191.4244544.6170723.0879079.9535970.0294510.0082760.0051421.27900.3300001.0262025-04-23 18:05:00apr_25FalseFalseFalseFalseFalse
8S32025-04-2318:14:005.310.070SakumonoFirst warm / stratified32.581356bdcmP4504-S3-250423-D30m-OA26.66851227.235.33344742.9817297.8740432286.7031137.8806922059.12809016.7360061876.847675165.5444093.9670852.63075310.9438090.0100000.0050000.0100000.10000.4150007.0202025-04-23 18:14:00apr_25TrueTrueTrueTrueFalse
9J12025-04-2511:11:005.41-0.180JamestownFirst warm / stratified2.197963surfaceP4504-J1-250425-D1m-OA28.93552228.835.29367912.3485347.8830002125.0233237.8809461892.61286214.6988181714.156037163.7580073.9594972.64409710.3059590.0450000.0088250.0055591.43000.1750006.1002025-04-25 11:11:00apr_25FalseFalseFalseFalseFalse
stationdatetimelatlonlocseasondepth_mdepth_descsample_idtemp_wattemp_in_lbsal_watpresph_lbtaphtcco2hco3-co3-omega_caomega_arrevellenitrate_nitriteammoniumphosphatesilicatechlo2datetimecruisenitrate_nitrite_imputedammonium_imputedphosphate_imputedsilicate_imputedchl_imputed
20P22025-04-2611:32:005.500.195PrampramFirst warm / stratified47.293000dcmP4504-P2-250426-D65m-OA23.59930026.635.71290057.8145007.8845062219.1669647.9270011993.00157215.3847851816.783828160.8329593.8145782.50794010.8342070.0280880.0120780.0550001.34300.4200001.4322025-04-26 11:32:00apr_25FalseFalseFalseFalseFalse
21P22025-04-2611:49:005.500.195PrampramFirst warm / stratified73.834000bdcmP4504-P2-250426-D85m-OA19.35150026.535.95300084.5740007.7854252239.5396287.8869752058.88135819.2556451907.251764132.3739503.1051612.01916312.2509038.1100000.0271940.5230002.65400.3032690.9642025-04-26 11:49:00apr_25FalseFalseFalseFalseTrue
22P32025-04-2613:10:005.410.240PrampramFirst warm / stratified1.241800surfaceP4504-P3-250426-D1m-OA29.45490026.535.40540011.3900007.9759592317.5109807.9322052037.46982913.7565641823.489350200.2239154.8422243.2397859.8668720.0306650.0126930.0081241.36700.2750000.4962025-04-26 13:10:00apr_25FalseFalseFalseFalseFalse
23P32025-04-2613:28:005.410.240PrampramFirst warm / stratified70.087900dcmP4504-P3-250426-D60m-OA19.85010026.635.81640080.7967007.8510702332.3838707.9481672115.02869716.8605771940.683337157.4847833.7020542.40987011.4723485.1200000.0130940.3500001.93900.3500000.0282025-04-26 13:28:00apr_25FalseFalseFalseFalseFalse
24A12025-04-2711:00:005.660.330AyitepaFirst warm / stratified2.632352surfaceP4504-A1-250427-D1m-OA29.05710127.435.30068412.7864947.9269682265.5005847.9025242010.24694314.7565871812.666109182.8242484.4210872.95359110.2173290.0332810.0134870.0084221.31300.2550007.0002025-04-27 11:00:00apr_25FalseFalseFalseFalseFalse
25A12025-04-2711:21:005.660.330AyitepaFirst warm / stratified43.138458dcmP4504-A1-250427-D37m-OA25.34194727.135.57177053.6256297.9008812241.2956157.9252572002.29976214.9538601817.114173170.2317284.0556262.67967910.5668920.2226340.0623890.0230001.32050.6100005.2102025-04-27 11:21:00apr_25FalseFalseFalseFalseFalse
26A22025-04-2712:21:005.570.375AyitepaFirst warm / stratified1.134901surfaceP4504-A2-250427-D1m-OA29.40581327.535.40471611.2767327.9049382238.9667177.8770051996.00627015.4864841806.180048174.3397384.2158862.82024610.3900290.0372810.1119850.0087141.26050.2750004.1202025-04-27 12:21:00apr_25FalseFalseFalseFalseFalse
27A22025-04-2712:48:005.570.375AyitepaFirst warm / stratified70.211005dcmP4504-A2-250427-D57m-OA19.77265527.735.82939780.9207597.7805552247.6335967.8935072061.53859818.8156911906.652795136.0701113.1980192.08138512.10331110.9750000.0142540.4980002.13700.3650005.0202025-04-27 12:48:00apr_25FalseFalseFalseFalseFalse
28A32025-04-2714:16:005.480.420AyitepaFirst warm / stratified1.281243surfaceP4504-A3-250427-D1m-OA29.55037128.035.24725511.4242777.9175862232.9934007.8948021980.89283414.6813281786.286946179.9245604.3584242.91627510.1827792.4150000.0146290.0850001.08050.3050006.2302025-04-27 14:16:00apr_25FalseFalseFalseFalseFalse
29A32025-04-2714:28:005.480.420AyitepaFirst warm / stratified70.817634dcmP4504-A3-250427-D70m-OA20.14835027.935.71711681.5323757.8290812209.5685647.9403672002.01924916.1663041838.028157147.8247883.4783052.26570511.3615701.1296580.0164540.1009651.11000.4000009.4202025-04-27 14:28:00apr_25FalseFalseFalseFalseFalse